diff --git a/week1/community-contributions/wk1-day1-deepseek-stream-summarize.ipynb b/week1/community-contributions/wk1-day1-deepseek-stream-summarize.ipynb index 1c641f5..6904a66 100644 --- a/week1/community-contributions/wk1-day1-deepseek-stream-summarize.ipynb +++ b/week1/community-contributions/wk1-day1-deepseek-stream-summarize.ipynb @@ -2,47 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "a767b6bc-65fe-42b2-988f-efd54125114f", "metadata": {}, - "outputs": [ - { - "data": { - "text/markdown": [ - "```markdown\n", - "# Summary of \"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning\"\n", - "\n", - "## Overview\n", - "The paper introduces **DeepSeek-R1**, a first-generation reasoning model developed by DeepSeek-AI. The model is designed to enhance reasoning capabilities in large language models (LLMs) using reinforcement learning (RL). Two versions are presented:\n", - "- **DeepSeek-R1-Zero**: A model trained via large-scale RL without supervised fine-tuning (SFT), showcasing strong reasoning abilities but facing challenges like poor readability and language mixing.\n", - "- **DeepSeek-R1**: An improved version incorporating multi-stage training and cold-start data before RL, achieving performance comparable to OpenAI's models on reasoning tasks.\n", - "\n", - "## Key Contributions\n", - "- Open-sourcing of **DeepSeek-R1-Zero**, **DeepSeek-R1**, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama architectures.\n", - "- The models are made available to support the research community.\n", - "\n", - "## Community Engagement\n", - "- The paper has been widely discussed and recommended, with 216 upvotes and 45 models citing it.\n", - "- Additional resources, including a video review and articles, are available through external links provided by the community.\n", - "\n", - "## Related Research\n", - "The paper is part of a broader trend in enhancing LLMs' reasoning abilities, with related works such as:\n", - "- **Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization (2024)**\n", - "- **Offline Reinforcement Learning for LLM Multi-Step Reasoning (2024)**\n", - "- **Reasoning Language Models: A Blueprint (2025)**\n", - "\n", - "## Availability\n", - "- The paper and models are accessible on [GitHub](https://github.com/deepseek-ai/DeepSeek-R1) and the [arXiv page](https://arxiv.org/abs/2501.12948).\n", - "```" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import os\n", "import requests\n", @@ -132,11 +95,19 @@ "\n", "display_summary()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01c9e5e7-7510-43ef-bb9c-aa44b15d39a7", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "llms", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" },